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This week from AGU: Long-term ecological research, predicting cholera outbreaks

American Geophysical Union

  • From this week's Eos: Long-Term Ecological Research and Network-Level Science

Imagine if we had the ability to track how a wide range of ecosystems was responding to global changes in real time. Such a tool would be particularly powerful if it coupled multiple decades of information about ecological responses to environmental change with large-scale, long-term experiments and models from dozens of different ecosystem types.

In fact, this tool exists: It is the Long-Term Ecological Research (LTER) Network, which will soon celebrate its 35th anniversary.

  • From AGU's journals: A model to predict when a cholera outbreak might hit the Congo

In 2011, as many as 600,000 people in 58 countries contracted cholera, with thousands succumbing to the disease. In most countries, cholera is rare. In others, like the Democratic Republic of the Congo, cholera is an endemic threat, always lurking in the background waiting for the right set of conditions to spark an outbreak.

In a new modeling study, Finger et al. determine the set of environmental conditions most likely to trigger a cholera epidemic in the area around Lake Kivu, a region in eastern Congo that is home to 1.8 million people and that sees recurrent outbreaks of the disease.

The bacterium that causes the disease, Vibrio cholerae, is most commonly spread through contaminated water. As such, unfavorable environmental conditions together with already-poor sanitation infrastructure may trigger an outbreak. Using records of monthly cholera incidence from 2004 to 2011, the authors tested how a range of environmental factors contributed to the spread of the disease.

The authors combined a range of factors, including satellite-based measurements of the concentration of plankton at the surface of nearby Lake Kivu, precipitation, seasonality, and others, to build 64 unique model constructions--one model for each possible configuration. They tested these models against the cholera incidence observations to identify which factors are most important to cholera outbreaks near Lake Kivu.

The model that best fits the cholera data considers the state of the El Niño-Southern Oscillation, the amount of precipitation, and a simplified parameterization of human mobility. Because these environmental conditions could possibly be predicted in advance, tracking these parameters could potentially give health managers much-needed warning time to prepare for an outbreak.


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